>But it can’t do the essential work of complexity management
I think this is the "closing the loop" ( https://en.wikipedia.org/wiki/Control_loop#Open-loop_and_clo... ) moment for coding AI.
All pieces are there, we just need to decide to do it. Today's AI are able to produce an increasing tangled mess of code. But it's also able to reorganize the code. It's also capable of writing test code, and assess the quality of the code. It's also capable to make architectural decision.
Today's AI code, is more like a Frankenstein's composition. But with the right prompt OODA loop and quality assessment rigor, it boils down to just having to sort and clean the junk pile faster than you produce it.
Once you have a coherent unified codebase, things get fast quickly, capabilities grows exponentially with the number of lines of code. Think of things like Julia Language or Wolfram Language.
Once you have a well written library or package, you are more than 95% there and you almost don't need AI to do the things you want to do.
I think this is the "closing the loop" ( https://en.wikipedia.org/wiki/Control_loop#Open-loop_and_clo... ) moment for coding AI.
All pieces are there, we just need to decide to do it. Today's AI are able to produce an increasing tangled mess of code. But it's also able to reorganize the code. It's also capable of writing test code, and assess the quality of the code. It's also capable to make architectural decision.
Today's AI code, is more like a Frankenstein's composition. But with the right prompt OODA loop and quality assessment rigor, it boils down to just having to sort and clean the junk pile faster than you produce it.
Once you have a coherent unified codebase, things get fast quickly, capabilities grows exponentially with the number of lines of code. Think of things like Julia Language or Wolfram Language.
Once you have a well written library or package, you are more than 95% there and you almost don't need AI to do the things you want to do.